






'''simple docstring'''





from collections import deque
def __UpperCAmelCase   (					A					:			str		)    ->   Union[str, Any]:
			UpperCAmelCase_      :       Tuple          =       len(A		)
			UpperCAmelCase_      :       List[Any]          =       deque()
			UpperCAmelCase_      :       str          =       [False for _ in range(A		)]
			UpperCAmelCase_      :       Tuple          =       [-1 for _ in range(A		)]
			UpperCAmelCase_      :       int          =       index_of[:]

			def strong_connect(A					:			List[Any]			,       A					:			Any			,       A					:			Dict		):
						UpperCAmelCase_      :       Dict          =       index  # the number when this node is seen
						UpperCAmelCase_      :       str          =       index  # lowest rank node reachable from here
						index += 1
						stack.append(A		)
						UpperCAmelCase_      :       str          =       True

						for w in g[v]:
									if index_of[w] == -1:
												UpperCAmelCase_      :       Tuple          =       strong_connect(A			,       A			,       A		)
												UpperCAmelCase_      :       List[Any]          =       (
												    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
												)
									elif on_stack[w]:
												UpperCAmelCase_      :       Dict          =       (
												    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
												)

						if lowlink_of[v] == index_of[v]:
									UpperCAmelCase_      :       List[Any]          =       []
									UpperCAmelCase_      :       Any          =       stack.pop()
									UpperCAmelCase_      :       Optional[int]          =       False
									component.append(A		)
									while w != v:
												UpperCAmelCase_      :       Tuple          =       stack.pop()
												UpperCAmelCase_      :       Optional[Any]          =       False
												component.append(A		)
									components.append(A		)
						return index

			UpperCAmelCase_      :       List[str]          =       []
			for v in range(A		):
						if index_of[v] == -1:
									strong_connect(A			,       0			,       A		)

			return components
def __UpperCAmelCase   (					A					:			int			,       A					:			Any		)    ->   List[str]:
			UpperCAmelCase_      :       int          =       [[] for _ in range(A		)]
			for u, v in edges:
						g[u].append(A		)
			return g


if __name__ == "__main__":
						# Test
						_UpperCamelCase						:						Dict  =					7
						_UpperCamelCase						:						Any  =					[0, 0, 1, 2, 3, 3, 4, 4, 6]
						_UpperCamelCase						:						Tuple  =					[1, 3, 2, 0, 1, 4, 5, 6, 5]
						_UpperCamelCase						:						List[str]  =					[(u, v) for u, v in zip(source, target)]
						_UpperCamelCase						:						Optional[Any]  =					create_graph(n_vertices, edges)

						assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)










'''simple docstring'''





import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase						:						Optional[Any]  =					logging.get_logger(__name__)

_UpperCamelCase						:						Dict  =					{
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}


class  snake_case__      (					UpperCamelCase):
					a_        =					"mvp"
					a_        =					["past_key_values"]
					a_        =					{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




					def __init__(  self  :		Any				, _A  :		List[str]=5_02_67				, _A  :		Dict=10_24				, _A  :		List[str]=12				, _A  :		Dict=40_96				, _A  :		Optional[int]=16				, _A  :		Optional[Any]=12				, _A  :		int=40_96				, _A  :		Dict=16				, _A  :		Union[str, Any]=0.0				, _A  :		Optional[Any]=0.0				, _A  :		int="gelu"				, _A  :		Union[str, Any]=10_24				, _A  :		int=0.1				, _A  :		Optional[Any]=0.0				, _A  :		Optional[Any]=0.0				, _A  :		Optional[Any]=0.02				, _A  :		Optional[Any]=0.0				, _A  :		str=False				, _A  :		List[Any]=True				, _A  :		Optional[int]=1				, _A  :		Tuple=0				, _A  :		Dict=2				, _A  :		Dict=True				, _A  :		int=2				, _A  :		Tuple=2				, _A  :		List[str]=False				, _A  :		Any=1_00				, _A  :		Union[str, Any]=8_00				, **_A  :		List[str]				, )    ->							int:
								UpperCAmelCase_      :       Union[str, Any]          =       vocab_size
								UpperCAmelCase_      :       int          =       max_position_embeddings
								UpperCAmelCase_      :       Dict          =       d_model
								UpperCAmelCase_      :       Dict          =       encoder_ffn_dim
								UpperCAmelCase_      :       Optional[Any]          =       encoder_layers
								UpperCAmelCase_      :       Tuple          =       encoder_attention_heads
								UpperCAmelCase_      :       List[Any]          =       decoder_ffn_dim
								UpperCAmelCase_      :       List[Any]          =       decoder_layers
								UpperCAmelCase_      :       Dict          =       decoder_attention_heads
								UpperCAmelCase_      :       str          =       dropout
								UpperCAmelCase_      :       Optional[Any]          =       attention_dropout
								UpperCAmelCase_      :       Optional[Any]          =       activation_dropout
								UpperCAmelCase_      :       Union[str, Any]          =       activation_function
								UpperCAmelCase_      :       Union[str, Any]          =       init_std
								UpperCAmelCase_      :       Optional[int]          =       encoder_layerdrop
								UpperCAmelCase_      :       Optional[int]          =       decoder_layerdrop
								UpperCAmelCase_      :       Dict          =       classifier_dropout
								UpperCAmelCase_      :       List[Any]          =       use_cache
								UpperCAmelCase_      :       Dict          =       encoder_layers
								UpperCAmelCase_      :       Tuple          =       scale_embedding  # scale factor will be sqrt(d_model) if True
								UpperCAmelCase_      :       int          =       use_prompt
								UpperCAmelCase_      :       Tuple          =       prompt_length
								UpperCAmelCase_      :       str          =       prompt_mid_dim

								super().__init__(
								    pad_token_id=_A				, bos_token_id=_A				, eos_token_id=_A				, is_encoder_decoder=_A				, decoder_start_token_id=_A				, forced_eos_token_id=_A				, **_A				, )

								if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''				, _A      ):
											UpperCAmelCase_      :       Optional[Any]          =       self.bos_token_id
											warnings.warn(
											    F"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
											    '''The config can simply be saved and uploaded again to be fixed.'''      )


